• Original Image—Original input image.

Smoothing

• Lowpass—Lowpass filtering. Smoothes images by eliminating details and blurring edges.
• Local Average—Local Averaging of the image pixels based on the kernel.
• Gaussian—Gaussian filtering based on the kernel. Attenuates the variations of light intensity in the neighborhood of a pixel. The Gaussian kernel has the following model: a d c b x b c d a where a, b, c, and d are integers and x>1.
• Median—Median filtering. Each pixel is assigned the median value of its neighborhood.

Edge Detection

• Laplacian—Laplacian filtering. Extracts the contour of objects and outlines details. The Laplacian filter kernel has the following model:

  a d c b x b c d a

  where a, b, c, and d are integers and x is greater than or equal to the sum of the absolute values of the outer coefficients.

• Diff—Differentiation filtering. Produces continuous contours by highlighting each pixel where an intensity variation occurs between itself and its three upper left neighbors.
• Prewitt—Prewitt filtering. A highpass filter that extracts the outer contours of objects.
• Sobel—Sobel filter. A highpass filter that extracts the outer contours of objects.
• Roberts—Roberts filter to detect edges. Outlines the contours that highlight pixels where an intensity variation occurs along the diagonal axes.

Convolution

• Highlight Details—Convolution kernel that highlights the edges of an image.
• Custom—Custom filtering using the kernel coefficients and size that you specify.

• 3×3
• 5×5
• 7×7

A default Kernel is used for each filter type. If you modify the Kernel, Filter Type will change to Custom Filter.